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The 17 Sustainable Development Goals (SDGs) proposed by the UN are not independent of each other. In this article, we analyze the reciprocal influence of 17 SDGs, on the basis of which we propose Network-based Priority Selection Algorithm (NPSA), in which static and dynamic networks are constructed. The static network is established to describe the fundamental interaction between 17 SDGs by using Cross-impact Matrix for Targets. Fiscal expenditure, main driving force of the development of 17 SDGs, is mainly invested in ten fields. So we set up a Field-Goal Map by counting the number of targets they include in common. Then we establish four priority selection indicators, including centrality, system impact, urgency and development-based modification. According to the various developing level of different countries, we assign personalized weight vectors for them to synthesize the influence of the four indicators using the Entropy Weight Method. Then we get priority score of each goal and its rank for each year. We quantitatively simulate system dynamics to implement scenario analysis on a ten-year horizon. Once a year, we re-rank the priority of 17 SDGs and then use conservative, steady, or aggressive strategies to allocate financial expenditure. Considering the delay effect between investment and reward, we propose Delay-based Breadth-First Search Algorithm (D-BFS) to update our network, broadcasting the influence of one goal to another once a month. Logistic curve in is used to simulate the S-shaped curve change of the degree of mutual influence between goals. We use the dynamic model to give guidance on financial investment for each goal in the next ten years. Moreover, facing global field mutation and crisis, the development strategy calculated by our model has excellent stability. The code is released at https://github.com/GoodMorningPeter/NPSA.
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